IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v28y2008i4p1099-1114.html

A Sensitivity Analysis of the Social Vulnerability Index

Author

Listed:
  • Mathew C. Schmidtlein
  • Roland C. Deutsch
  • Walter W. Piegorsch
  • Susan L. Cutter

Abstract

The Social Vulnerability Index (SoVI), created by Cutter et al. (2003), examined the spatial patterns of social vulnerability to natural hazards at the county level in the United States in order to describe and understand the social burdens of risk. The purpose of this article is to examine the sensitivity of quantitative features underlying the SoVI approach to changes in its construction, the scale at which it is applied, the set of variables used, and to various geographic contexts. First, the SoVI was calculated for multiple aggregation levels in the State of South Carolina and with a subset of the original variables to determine the impact of scalar and variable changes on index construction. Second, to test the sensitivity of the algorithm to changes in construction, and to determine if that sensitivity was constant in various geographic contexts, census data were collected at a submetropolitan level for three study sites: Charleston, SC; Los Angeles, CA; and New Orleans, LA. Fifty‐four unique variations of the SoVI were calculated for each study area and evaluated using factorial analysis. These results were then compared across study areas to evaluate the impact of changing geographic context. While decreases in the scale of aggregation were found to result in decreases in the variance explained by principal components analysis (PCA), and in increases in the variance of the resulting index values, the subjective interpretations yielded from the SoVI remained fairly stable. The algorithm's sensitivity to certain changes in index construction differed somewhat among the study areas. Understanding the impacts of changes in index construction and scale are crucial in increasing user confidence in metrics designed to represent the extremely complex phenomenon of social vulnerability.

Suggested Citation

  • Mathew C. Schmidtlein & Roland C. Deutsch & Walter W. Piegorsch & Susan L. Cutter, 2008. "A Sensitivity Analysis of the Social Vulnerability Index," Risk Analysis, John Wiley & Sons, vol. 28(4), pages 1099-1114, August.
    • Handle: RePEc:wly:riskan:v:28:y:2008:i:4:p:1099-1114
    DOI: 10.1111/j.1539-6924.2008.01072.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1539-6924.2008.01072.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1539-6924.2008.01072.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. George Ferguson, 1954. "The concept of parsimony in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 19(4), pages 281-290, December.
    2. Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 187-200, September.
    3. Walter W. Piegorsch & Susan L. Cutter & Frank Hardisty, 2007. "Benchmark Analysis for Quantifying Urban Vulnerability to Terrorist Incidents," Risk Analysis, John Wiley & Sons, vol. 27(6), pages 1411-1425, December.
    4. John Carroll, 1953. "An analytical solution for approximating simple structure in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 23-38, March.
    5. Susan L. Cutter & Bryan J. Boruff & W. Lynn Shirley, 2003. "Social Vulnerability to Environmental Hazards," Social Science Quarterly, Southwestern Social Science Association, vol. 84(2), pages 242-261, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Henk Kiers, 1997. "Three-mode orthomax rotation," Psychometrika, Springer;The Psychometric Society, vol. 62(4), pages 579-598, December.
    2. Douglas Clarkson & Robert Jennrich, 1988. "Quartic rotation criteria and algorithms," Psychometrika, Springer;The Psychometric Society, vol. 53(2), pages 251-259, June.
    3. Thomas Despois & Catherine Doz, 2023. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 533-555, June.
    4. Gainbi Park & Zengwang Xu, 2022. "The constituent components and local indicator variables of social vulnerability index," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 95-120, January.
    5. Conti, Gabriella & Frühwirth-Schnatter, Sylvia & Heckman, James J. & Piatek, Rémi, 2014. "Bayesian exploratory factor analysis," Journal of Econometrics, Elsevier, vol. 183(1), pages 31-57.
    6. Jin, Shaobo & Moustaki, Irini & Yang-Wallentin, Fan, 2018. "Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case," LSE Research Online Documents on Economics 88118, London School of Economics and Political Science, LSE Library.
    7. Zhe Huang & Emily Ying Yang Chan & Chi Shing Wong & Benny Chung Ying Zee, 2021. "Clustering of Socioeconomic Data in Hong Kong for Planning Better Community Health Protection," IJERPH, MDPI, vol. 18(23), pages 1-21, November.
    8. Jingyu Liu & Walter W. Piegorsch & A. Grant Schissler & Susan L. Cutter, 2018. "Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 803-823, June.
    9. D. Saunders, 1961. "The rationale for an “oblimax” method of transformation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 26(3), pages 317-324, September.
    10. Kei Hirose & Yoshikazu Terada, 2023. "Sparse and Simple Structure Estimation via Prenet Penalization," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1381-1406, December.
    11. Coen Bernaards & Robert Jennrich, 2003. "Orthomax rotation and perfect simple structure," Psychometrika, Springer;The Psychometric Society, vol. 68(4), pages 585-588, December.
    12. Simon Freyaldenhoven, 2020. "Identification Through Sparsity in Factor Models," Working Papers 20-25, Federal Reserve Bank of Philadelphia.
    13. Urbano Lorenzo-Seva, 2003. "A factor simplicity index," Psychometrika, Springer;The Psychometric Society, vol. 68(1), pages 49-60, March.
    14. Paul M. Johnson & Corey E. Brady & Craig Philip & Hiba Baroud & Janey V. Camp & Mark Abkowitz, 2020. "A Factor Analysis Approach Toward Reconciling Community Vulnerability and Resilience Indices for Natural Hazards," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1795-1810, September.
    15. Henk Kiers & Jos Berge, 1994. "The Harris-Kaiser independent cluster rotation as a method for rotation to simple component weights," Psychometrika, Springer;The Psychometric Society, vol. 59(1), pages 81-90, March.
    16. T. F. Cox & D. S. Arnold, 2018. "Simple components," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 83-99, January.
    17. Robert Sokal, 1958. "Thurstone's analytical method for simple structure and a mass modification thereof," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 237-257, September.
    18. Luke Mosley & Tak-Shing Chan & Alex Gibberd, 2023. "sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings," Papers 2303.14125, arXiv.org.
    19. Emily Yang Ying Chan & Zhe Huang & Holly Ching Yu Lam & Carol Ka Po Wong & Qiang Zou, 2019. "Health Vulnerability Index for Disaster Risk Reduction: Application in Belt and Road Initiative (BRI) Region," IJERPH, MDPI, vol. 16(3), pages 1-16, January.
    20. Thomas Despois & Catherine Doz, 2022. "Identifying and interpreting the factors in factor models via sparsity : Different approaches," Working Papers halshs-03626503, HAL.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:riskan:v:28:y:2008:i:4:p:1099-1114. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.